KiDS-1000: Cosmology with improved cosmic shear measurements

Astronomy & Astrophysics EDP Sciences 679 (2023) A133-A133

Authors:

Shun-Sheng Li, Henk Hoekstra, Konrad Kuijken, Marika Asgari, Maciej Bilicki, Benjamin Giblin, Catherine Heymans, Hendrik Hildebrandt, Benjamin Joachimi, Lance Miller, Jan Luca van den Busch, Angus H Wright, Arun Kannawadi, Robert Reischke, HuanYuan Shan

Abstract:

We present refined cosmological parameter constraints derived from a cosmic shear analysis of the fourth data release of the Kilo-Degree Survey (KiDS-1000). Our main improvements include enhanced galaxy shape measurements made possible by an updated version of the lensfit code and improved shear calibration achieved with a newly developed suite of multi-band image simulations. Additionally, we incorporated recent advancements in cosmological inference from the joint Dark Energy Survey Year 3 and KiDS-1000 cosmic shear analysis. Assuming a spatially flat standard cosmological model, we constrain $S_8\equiv\sigma_8(\Omega_{\rm m}/0.3)^{0.5} = 0.776_{-0.027-0.003}^{+0.029+0.002}$, where the second set of uncertainties accounts for the systematic uncertainties within the shear calibration. These systematic uncertainties stem from minor deviations from realism in the image simulations and the sensitivity of the shear measurement algorithm to the morphology of the galaxy sample. Despite these changes, our results align with previous KiDS studies and other weak lensing surveys, and we find a ${\sim}2.3\sigma$ level of tension with the Planck cosmic microwave background constraints on $S_8$.Comment: 20 pages, 13 figures, 4 tables, minor revisions to match the final accepted versio

Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 526:3 (2023) 4768-4786

Authors:

Mike Walmsley, Tobias Géron, Sandor Kruk, Anna MM Scaife, Chris Lintott, Karen L Masters, James M Dawson, Hugh Dickinson, Lucy Fortson, Izzy L Garland, Kameswara Mantha, David O’Ryan, Jürgen Popp, Brooke Simmons, Elisabeth M Baeten, Christine Macmillan

Signatures of feedback in the spectacular extended emission region of NGC 5972

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 526:3 (2023) 4174-4191

Authors:

Thomas Harvey, W Peter Maksym, William Keel, Michael Koss, Vardha N Bennert, S Drew Chojnowski, Ezequiel Treister, Carolina Finlez, Chris J Lintott, Alexei Moiseev, Brooke D Simmons, Lia F Sartori, Megan Urry

From particles to orbits: precise dark matter density profiles using dynamical information

(2023)

Authors:

Claudia Muni, Andrew Pontzen, Jason L Sanders, Martin P Rey, Justin I Read, Oscar Agertz

The Simons Observatory: a new open-source power spectrum pipeline applied to the Planck legacy data

Journal of Cosmology and Astroparticle Physics IOP Publishing 2023:09 (2023) 048-048

Authors:

Zack Li, Thibaut Louis, Erminia Calabrese, Hidde Jense, David Alonso, Zachary Atkins, J Richard Bond, Steve K Choi, Jo Dunkley, Giulio Fabbian, Xavier Garrido, Andrew H Jaffe, Mathew S Madhavacheril, P Daniel Meerburg, Umberto Natale, Frank J Qu

Abstract:

We present a reproduction of the Planck 2018 angular power spectra at ℓ > 30, and associated covariance matrices, for intensity and polarization maps at 100, 143 and 217 GHz. This uses a new, publicly available, pipeline that is part of the PSpipe package. As a test case we use the same input maps, ancillary products, and analysis choices as in the Planck 2018 analysis, and find that we can reproduce the spectra to 0.1σ precision, and the covariance matrices to 10%. We show that cosmological parameters estimated from our re-derived products agree with the public Planck products to 0.1σ, providing an independent cross-check of the Planck team's analysis. Going forward, the publicly-available code can be easily adapted to use alternative input maps, data selections and analysis choices, for future optimal analysis of Planck data with new ground-based Cosmic Microwave Background data.